Multivariate Statistical Methods in Quality Management

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McGraw-Hill Education, Mar 17, 2004 - Technology & Engineering - 299 pages


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UPGRADE MANUFACTURING AND SERVICE PERFORMANCE WITH POWERFUL STATISTICAL TOOLS

There's no better way to master the most rigorous statistical methods available for analyzing the performance of complex systems -- Multivariate Statistical Methods in Quality Management teaches powerful analytic tools for troubleshooting, root cause analysis, process control, quality improvement, and many other applications. Written by statistics experts who specialize in reliability and quality engineering, this unique resource introduces the fundamentals and then demonstrates how to:

*Choose the best method for each data set
* Make complex data intelligible with graphical tools
* Coax important hidden information from data with graphical 3-D software and numerical multivariate data stratification
* Perform data reduction with principal component analysis, factor analysis, and discriminant analysis
* Apply multivariate methods in Six Sigma enterprises
* Get clarification from case studies, models, and 50 illustrations
* Uncover the source of problems and pinpoint solutions in arenas from manufacturing processes to sales performance and beyond

From inside the book

 

Results 1-3 of 69

Page ix
... Principal Component Analysis and Factor Analysis 5.1 Introduction 5.2 Principal Component Analysis Based on Covariance Matrices 5.2.1 Two mathematical representations of principal component analysis 86 86 95 88855 5.2.2 Properties of ...
Page 99
... analysis is introduced in Sec . 5.6 . One spe- cial feature of factor analysis is factor rotation . Factor rotation is a ... Principal Component Analysis and Factor Analysis 99 Principal Component Analysis Based on Covariance Matrices.
Page 109
... principal component analysis consists of the following linear transformation : Y = CT Z ( 5.18 ) where Z = ( Z1 , Z2 , ... , Z ) Y = ( Y1 , ... , Y2 ) T C = ( Cij ) pxp and C is ... Principal Component Analysis and Factor Analysis 109.

Where's the rest of this book?

Contents

Multivariate Statistical Methods and Quality
1
Graphical Multivariate Data Display and Data Stratification
2
Multivariate Analysis of Variance
81
Copyright

17 other sections not shown

Other editions - View all

Multivariate Statistical Methods in Quality Management
Kai Yang,Jayant Trewn
Limited preview - 2004
Multivariate Statistical Methods in Quality Management
Kai Yang,Jayant Trewn
Snippet view - 2004
Multivariate Statistical Methods in Quality Management
Kai Yang,Jayant Trewn
No preview available - 2004

Common terms and phrases

abnormal group Canola centroid cluster analysis coefficients common factor analysis computed control chart control limits correlation matrix Cottonseed data mining degradation dependent variables dimensional variation discriminant analysis distance between cluster eigenvalues equations estimate Euclidean distance example factor analysis Factor Loadings Figure indirect effect IRLEDs linkage method luminous power Mahalanobis-Taguchi system MANOVA mean vector measurement data measuring points MINITAB multivariate data set multivariate normal distribution multivariate process control multivariate random multivariate random variables multivariate statistical multivariate statistical methods N₁ N₂ objects observations orthogonal array parameters path analysis path diagram population principal component analysis principal component score pulse random variables reference sample rotation sample covariance matrix scaled Mahalanobis distance shown in Fig Six Sigma Soybean standard deviation standardized variables structural model subgroup Table Taguchi temperature tested at 280 underbody univariate variance variation mode chart waist weight X₂ Y₁ µ₁ µ₂

About the author (2004)

Kai Yang, Ph.D., has consulted extensively in many areas of quality and reliability engineering. He is Associate Professor of Industrial and Manufacturing Engineering at Wayne State University, Detroit, Michigan. He lives in West Bloomfield, Michigan.

Jayant Trewn, Ph.D., is a research faculty member at Beaumont Hospital in Royal Oak, Michigan. He is responsible for implementing cutting edge industrial engineering tools in hospital and health care management. Dr. Trewn was a Director of Quality and Productivity Improvement at Vetri Systems, a Lason Company. He was responsible for business process design and improvement in the global business environment.

Bibliographic information

TitleMultivariate Statistical Methods in Quality Management
AuthorsKai Yang, Jayant Trewn
Editionillustrated
PublisherMcGraw-Hill Education, 2004
Original fromthe University of Michigan
Digitized20 Nov 2007
ISBN0071432086, 9780071432085
Length299 pages
Subjects ›  › 

Mathematics / Probability & Statistics / General
Technology & Engineering / Engineering (General)
  
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